Investigation of viscoelastic fracture fields in asphalt mixtures using digital image correlation

In this work we have studied the fracture behavior of asphalt mixtures, a heterogeneous mix of hard aggregates (usually in the form of crushed quarried rock) with a petroleum based asphalt binder, used in paving applications. Specifically, we studied the dependence of asphalt mixes’ fracture response on loading rate, temperature, and recycled content—the latter used primarily to replace virgin materials like aggregates and binder. Fracture tests were conducted on semi-circular bend edge cracked specimens obtained from mixes with different compositions, and the fracture event was recorded with a camera to allow for digital image correlation (DIC) measurements. DIC, with a spatial resolution of about 40 \(\upmu \)m/pixel, measured the far-field strain and displacement fields developing around a preexisting notch tip. Our focus here is on characterizing the material behavior by quantifying its viscoelastic response and fracture properties. The elastic–viscoelastic correspondence principle was used to extract viscous and elastic components from the full-field DIC-measured strain and displacement fields. Various energy dissipation mechanisms other than the fracture itself were evaluated. Stress–strain response and energy dissipated in the far-field regions were quantified. The pseudo-elastic stress intensity factor was then used to study the fracture properties, and quantify the effects on fracture properties of loading rate, temperature, and recycled content in the binder. It was seen that the viscoelastic characteristics of the material were a dominant factor in the material behavior obtained at room temperature. In general, the elastic component of the displacement was only up to about 30% of the total displacement, indicating a strong influence of viscoelasticity in this state. Loading rate, temperature and recycled asphalt shingles (RAS) content all affected the viscous response by introducing more elastic response when loading rate or recycled content increased or when temperature decreased. It became clear from these macroscopic measurements that the increase of RAS content considerably embrittles the material producing less viscous effects and less energy dissipated in the far-field, almost comparable to reductions associated with the loading rate increase (from 6.25 to 50 mm/min) or the temperature change (\(-12\) to \(25\,{^{\circ }}\)C).     

3-D Reconstruction of Urban Scenes from Image Sequences

In this paper, we address the problem of the recovery of a realistic textured model of a scene from a sequence of images, without any prior knowledge either about the parameters of the cameras or about their motion. We do not require any knowledge of the absolute coordinates of some control points in the scene to achieve this goal. First, using various computer vision tools, we establish correspondences between the images and recover the epipolar geometry, from which we show how to compute the complete set of perspective projection matrices for all camera positions. Then, we proceed to reconstruct the geometry of the scene. We show how to rely on information of the scene such as parallel lines or known angles in order to reconstruct the geometry of the scene up to, respectively, an unknown affine transformation or an unknown similitude. Alternatively, if this information is not available, we can still recover the Euclidean structure of the scene through the techniques of self-calibration. The scene geometry is modeled as a set of polyhedra. Textures to be mapped on the scene polygons are extracted automatically from the images. We show how several images can be combined through mosaicing in order to automatically remove visual artifacts such as pedestrians or trees from the textures.This vision system has been implemented as a vision server, which provides to a CAD-CAM modeler geometry or texture information extracted from the set of images. The whole system allows efficient and fast production of scene models of high quality for such applications as simulation, virtual, or augmented reality.     

The 2-block superoptimalAAK problem

This paper presents a solution to a superoptimal version of the 2-blockAAK problem: Given a rational and antistable matrix functionR(s)=[R ₁₁(s)R ₁₂(s)] and a nonnegative integerk, find all superoptimal approximationsQ(s), with no more thank poles in the right-half complex plane, that minimize the supremum, over the imaginary axis, of the singular values of the error functionE(s)=[R ₁₁ (s) R ₁₂ (s)+Q(s)], with respect to lexicographic ordering. Conditions are given for which the superoptimal approximation is unique. In addition, ana priori upper bound on the MacMillan degree of the approximation is provided. The algorithm may be stopped after minimizing a given number of the singular values. This premature termination carries with it a predictable reduction in the MacMillan degree of the approximation. The algorithm only requires standard linear algebraic computations, and is therefore easily implemented. This work has been supported by Engineering and Physical Sciences Research Council Grant GR/J42533.     

A new finite volume method for flux-gradient and source-term balancing in shallow water equations

We present a new finite volume method for flux-gradient and source-term balancing in the numerical solution of shallow water equations on nonflat topography. The method consists of a predictor stage for discretization of gradient terms and a corrector stage for treatment of source terms. The numerical fluxes at the interfaces of each triangle are reconstructed using an upwind scheme along with slope limiters to obtain high accuracy in the method. Using a three-sided decomposition, the source terms are balanced in the corrector stage. The proposed method is implemented on unstructured meshes and verifies the exact conservation property (C-property). We also discuss a mesh adaptive procedure by monitoring the water depth in the computational domain. Several standard test examples are used to verify high accuracy, exact C-property and good resolution properties for smooth and discontinuous solutions.     

Daily prediction of short-term trends of crude oil prices using neural networks exploiting multimarket dynamics

This paper documents a systematic investigation on the predictability of short-term trends of crude oil prices on a daily basis. In stark contrast with longer-term predictions of crude oil prices, short-term prediction with time horizons of 1–3 days posits an important problem that is quite different from what has been studied in the literature. The problem of such short-term predicability is tackled through two aspects. The first is to examine the existence of linear or nonlinear dynamic processes in crude oil prices. This sub-problem is addressed with statistical analysis involving the Brock-Dechert-Scheinkman test for nonlinearity. The second aspect is to test the capability of artificial neural networks (ANN) for modeling the implicit nonlinearity for prediction. Four experimental models are designed and tested with historical data: (1) using only the lagged returns of filtered crude oil prices as input to predict the returns of the next days; this is used as the benchmark, (2) using only the information set of filtered crude oil futures price as input, (3) combining the inputs from the benchmark and second models, and (4) combing the inputs from the benchmark model and the intermarket information. In order to filter out the noise in the original price data, the moving averages of prices are used for all the experiments. The results provided sufficient evidence to the predictability of crude oil prices using ANN with an out-of-sample hit rate of 80%, 70%, and 61% for each of the next three days’ trends.     

Causal state-feedback parameterizations in robust model predictive control

In this paper, we investigate the problem of nonlinearity (and non-convexity) typically associated with linear state-feedback parameterizations in the Robust Model Predictive Control (RMPC) for uncertain systems. In particular, we propose two tractable approaches to compute an RMPC controller–consisting of both a causal, state-feedback gain and a control-perturbation component–for linear, discrete-time systems involving bounded disturbances and norm-bounded structured model-uncertainties along with hard constraints on the input and state. Both the state-feedback gain and the control-perturbation are explicitly considered as decision variables in the online optimization while avoiding nonlinearity and non-convexity in the formulation. The proposed RMPC controller–computed through LMI optimizations–is responsible for steering the uncertain system state to a terminal invariant set. Numerical examples from the literature demonstrate the advantages of the proposed scheme.     

Legal Implications of the Use of Big Data in the Transboundary Water Context

Water Resources Management - This article examines the potential legal implications of the adoption and implementation of transboundary water agreements and international water conventions as a...     

Lipid oxidation in glassy and rubbery-state starch extrudates

This work describes the principle of protecting polyunsaturated fatty acids by holding them in the low moisture/solid/glassy-state starch matrix. One strategy already employed commercially is to encapsulate oil droplets within a solid wall that is highly impermeable to oxygen. These microencapsulated powders can then be added to foods. A shorter route would be to add PUFA-rich oils directly into a food formulation during the processing of a low moisture product. This should effectively encapsulate the valuable oils and protect them from oxidation. ω-6 Linoleic acid was incorporated into a waxy maize starch matrix via extrusion cooking. Linoleic acid oxidation occurred when this model food system was held in both the glassy and rubbery states (0.3 and 0.95 Aw, respectively) at 50 °C. The initial oxidation, not surprisingly, occurs near the surface, but interestingly the highest initial rate of lipid oxidation occurred, not in the rubbery samples, but in glassy state starch extrudates with surface micro-cracks.     

Electrical properties and longitudinal modulus of superconductor/polymer NdBa2Cu3O7−δ/PVC composites

High temperature superconductor NdBa₂Cu₃O_7–/poly(vinyl chloride) (Nd123/PVC) composites have been prepared by cold press and hot press methods. Addition of PVC resulted in increase of the electrical resistivity with percolation occurring between 0.3 and 0.5 Nd123 volume fraction for both preparation methods. Although the samples showed some form of magnetic levitation at liquid nitrogen temperature, they do not show any zero-resistance temperature indicating the lack of effective superconducting percolative path. The density deviates from the linear calculated value with increasing Nd123 content at 0.3 volume fraction for the cold press and at 0.6 for the hot pressed samples due to increasing porosity. X-ray powder diffraction patterns suggest that the Nd123 crystals tend to align when the composites are prepared by hot press method. A resistance anomaly is observed at 90 K in the hot press composites indicating improved electrical contact between the aligned Nd123 grains. The longitudinal modulus increases as Nd123 content is increased.